Reliable Renewable Generation and Transmission Expansion Planning: Co-Optimizing System's Resources for Meeting Renewable Targets

Moreira, Alexandre; Pozo, David; Street, Alexandre; Sauma, Enzo

doi:10.1109/tpwrs.2016.2631450

Cited by 170 publications

(100 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The best approximation quality is achieved when the reduced model is represented by a matrix response, where each generator responds to each variable source (i.e., each network node with significant variability) independently. These first results are encouraging as they open a multitude of opportunities to handle more complex and challenging settings, such as two-level and multistage planning problems of the type discussed in [4], [7], [19].…”

Section: B Comparison Of the Cloud-ac-opf And Ms-ac-opf Optimal Dispmentioning

confidence: 86%

Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization

Frolov

Roald

Chertkov

2019

2019 IEEE Milan PowerTech

View full text Add to dashboard Cite

Many practical planning and operational applications in power systems require simultaneous consideration of a large number of operating conditions or Multi-Scenario AC-Optimal Power Flow (MS-AC-OPF) solution. However, when the number of exogenously prescribed conditions is large, solving the problem as a collection of single AC-OPFs may be timeconsuming or simply intractable computationally. In this paper, we suggest a model reduction approach, coined Cloud-AC-OPF, which replaces a collection of samples by their compact representation in terms of mean and standard deviation. Instead of determining an optimal generation dispatch for each sample individually, we parametrize the generation dispatch as an affine function. The Cloud-AC-OPF is mathematically similar to a generalized Chance-Constrained AC-OPF (CC-AC-OPF) of the type recently discussed in the literature, but conceptually different as it discusses applications to long-term planning. We further propose a tractable formulation and implementation, and illustrate our construction on the example of 30-bus IEEE model.

show abstract

Section: B Comparison Of the Cloud-ac-opf And Ms-ac-opf Optimal Dispmentioning

confidence: 86%

Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization

Frolov

Roald

Chertkov

2019

2019 IEEE Milan PowerTech

View full text Add to dashboard Cite

show abstract

“…For example, the large increase in renewable generation (RG) has introduced new levels of intermittency and unpredictability to electrical systems. These new aspects have motivated a change in numerous paradigms for short-term operation as well as for the planning of transmission networks (see [5], [6] and references therein).…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

“…This fact has motivated the the recent literature on adaptive robust optimization (ARO) applied to TEP. In ARO-TEP problems, uncertainties are, in general, represented by polyhedral uncertainty sets relying on a few assumptions about the uncertainty factors (see [11] and [5]). As a consequence, the solution to the ARO-TEP problem focuses on achieving the best performance in the worst-case scenario at the expense of other scenarios.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

“…It is worth mentioning that while part of the recent literature on ARO have considered probability agnostic models (see [11], [5], and [4]), relevant efforts have been made to account for the information extracted from data to devise more realistic descriptions of the short-term uncertainty. We refer the interested reader to [12], [13], and [14] for applications in short-term operational models and to [9] for a hybridrobust-and-stochastic approach applied to the TEP problem.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Velloso

Pozo

Street

2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

We present a distributionally robust optimization (DRO) approach for the transmission expansion planning problem, considering both long-and short-term uncertainties on the system demand and renewable generation. On the longterm level, as it is customary in industry applications, the deep uncertainties arising from social and economic transformations, political and environmental issues, and technology disruptions are addressed by long-term scenarios devised by experts. The system planner is then allowed to consider exogenous long-term scenarios containing partial information about the random parameters, namely, the average and the support set. For each constructed long-term scenario, a conditional ambiguity set is used to model the incomplete knowledge about the probability distribution of the uncertain parameters in the short-term. Consequently, the mathematical problem is formulated as a DRO model with multiple conditional ambiguity sets. The resulting infinite-dimensional problem is recast as an exact, although very large, finite-deterministic mixedinteger linear programming problem. To circumvent scalability issues, we propose a new enhanced-column-and-constraintgeneration (ECCG) decomposition approach with an additional Dantzig-Wolfe procedure. In comparison to existing methods, ECCG leads to a better representation of the recourse function and, consequently, tighter bounds. Numerical experiments based on the benchmark IEEE 118-bus system are reported to corroborate the effectiveness of the method.

show abstract

“…Têm-se também trabalhos na literatura atual que incluem realizar o planejamento da expansão da geração em conjunto junto com o PET, para assim, conseguir coordenar de melhor modo a interação entre a integração eólica e o planejamento da rede de transmissão no horizonte contemplado, como é exposto em [31], [32] e [33].…”

Section: Desenvolvimento Histórico -Estado Da Arteunclassified

Planejamento Da Expansão Da Transmissão Considerando a Intermitência Da Geração Eólica

ALMEIDA¹

View full text Add to dashboard Cite

Reliable Renewable Generation and Transmission Expansion Planning: Co-Optimizing System's Resources for Meeting Renewable Targets

Cited by 170 publications

References 23 publications

Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization

Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization

Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Planejamento Da Expansão Da Transmissão Considerando a Intermitência Da Geração Eólica

Contact Info

Product

Resources

About